Philippe Goulet Coulombe, Massimiliano Marcellino, Dalibor Stevanovic
arXiv 1 Mar 2021 · Econometrics · publishedNational Institute Economic Review (2021) · 1 citations (OpenAlex)
arXiv:2103.01201 · PDF · DOI · OpenAlex · Extracted main text
Based on evidence gathered from a newly built large macroeconomic data set for the UK, labeled UK-MD and comparable to similar datasets for the US and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McCracken, M. W. and Ng, S (2016) FRED-MD: A monthly database for macroeconomic research | 1.000 | 7 | 3 | 100% |
| 2 | Stock, J. H. and Watson, M. W (2002) Macroeconomic forecasting using diffusion indexes | 1.000 | 6 | 5 | 100% |
| 3 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., and Surprenant, S (2019) How is machine learning useful for macroeconomic forecasting? self | 1.000 | 5 | 3 | 100% |
| 4 | Goulet Coulombe, P (2020) The macroeconomy as a random forest | 1.000 | 5 | 3 | 100% |
| 5 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., and Surprenant, S (2020) Macroeconomic data transformations matter self | 0.928 | 4 | 3 | 100% |
| 6 | Stock, J. H. and Watson, M. W (2002) Forecasting using principal components from a large number of predictors | 0.928 | 4 | 3 | 100% |
| 7 | Kotchoni, R., Leroux, M., and Stevanovic, D (2019) Macroeconomic forecast accuracy in a data-rich environment self | 0.843 | 3 | 3 | 100% |
| 8 | McCracken, M. and Ng, S (2020) FRED-QD: A quarterly database for macroeconomic research | 0.843 | 3 | 3 | 100% |
| 9 | Fortin-Gagnon, O., Leroux, M., Stevanovic, D., and Surprenant, S (2018) A large canadian database for macroeconomic analysis self | 0.811 | 4 | 2 | 100% |
| 10 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 39 scored citations.
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